Time series decomposition using automatic learning techniques for predictive models
Artículo de revista
2020
Journal of Physics: Conference Series
This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition. The agricultural sector will be used as the study subject.
- Artículos científicos [3156]
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Time Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf
Título: Time Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf
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Título: Time Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf
Tamaño: 679.7Kb
PDFLEER EN FLIP
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